erf: Fit an extremal random forest (ERF)
In nicolagnecco/erf: Extreme Random Forest

erf	R Documentation

Fit an extremal random forest (ERF)

Fits an extremal random forest (ERF).

erf(
  X,
  Y,
  min.node.size = 5,
  lambda = 0.001,
  intermediate_estimator = c("grf", "neural_nets"),
  intermediate_quantile = 0.8
)

`X`	Numeric matrix of predictors, where each row corresponds to an observation and each column to a predictor.
`Y`	Numeric vector of responses.
`min.node.size`	Minimum number of observations in each tree leaf used to fit the similarity weights (see also `grf::quantile_forest()`). Nodes with size smaller than `min.node.size` can occur, as in the original randomForest package. Default is `5`.
`lambda`	Penalty for the shape parameter used in the weighted likelihood. Default is `0.001`.
`intermediate_estimator`	A character specifying the estimator used to fit the intermediate threshold. Options available are: `grf`, see `grf::quantile_forest()`. `neural_nets`, (coming soon).
`intermediate_quantile`	Intermediate quantile level, used to predict the intermediate threshold. For further information see \insertCitemerg2020;textualerf. Default is `0.8`.

An object with S3 class "erf". It is a named list with the following elements:

`quantile_forest`	An object with S3 class "`quantile_forest`" (see `grf::quantile_forest()`). This is a fitted generalized random forest that contains the similarity weights used in the log-likelihood.
`min.node.size`	Minimum number of observations in each tree leaf used to fit the `quantile_forest`.
`lambda`	Penalty for the shape parameter used in the weighted log-likelihood.
`intermediate_threshold`	An object with S3 class: "`quantile_forest`", if `intermediate_estimator = "grf"`. "`neural_nets`", if `⁠intermediate_estimator = "neural_nets⁠` (note: this option is not yet implemented) This is a fitted object used to predict the intermediate thresholds.
`intermediate_quantile`	Intermediate quantile level, used to predict the intermediate threshold.
`Q_X`	Vector with intermediate quantile predicted on the training data `X`.